In this paper, a hybrid genetic algorithm implemented in a grid environment to solve hard instances of the flexible flow shop scheduling problem with sequence-dependent setup times is introduced. The genetic algorithm takes advantage of the distributed computing power on the grid to apply a hybrid local search to each individual in the population and reach a near optimal solution in a reduced number of generations. Ant colony systems and simulated annealing are used to apply a combination of iterative and cooperative local searches, respectively. This algorithm is implemented using a master–slave scheme, where the master process distributes the population on the slave process and coordinates the communication on the computational grid elements. The experimental results point out that the proposed scheme obtains the upper bound in a broad set of test instances. Also, an efficiency analysis of the proposed algorithm indicates its competitive use of the computational resources of the grid.

In this paper, a solution is presented to the unrelated parallel machines problem that minimizes the total weighted completion time. Simulated annealing is applied to the problem, which is modeled as a weighted bipartite matching problem. Experimental results with benchmarks are presented, evaluating the efficiency and efficacy of the algorithm. It is then compared with an exact algorithm that solves the pondered model of integer linear programming. The results demonstrate that simulated annealing algorithm has high performance because for all the evaluated instances, it finds the optimum global solution.

This paper presents the application of the Weather Research and Forecasting (WRF version 3.5) with high spatial resolution (3 and 1 km) testing four Planet Boundary Layer (PBL) schemes to the complex topography of Mexico in different numerical experiments that have tried to find the best configuration. The WRF is a Numerical Weather Prediction (NWP) model giving support for weather forecasting and modeling in Chemical Transport (CTM) or Air Quality Models as CMAQ or CHIMERE. For the above, the objectives in this work were assess the performance, the best grid parameterization, meteorological initial conditions, temporal resolution, time step and PBL schemes used in order to minimize the execution time and to show changes in speedup and efficiency. Mare Nostrum III supercomputer was used for the computational processing in the cluster. The computational performance indicators were execution time, speedup and efficiency. The sensitivity analysis was performed using the mean bias (MB) and root mean square error (RMSE) from variables obtained by WRFv3.5 such as temperature, wind-speed, sea level pressure and the METAR Veracruz airport station observations. The results show that Mellor-Yamada-Janjic (MYJ) scheme was better in computational parallel execution with at least 8 processors and a time step of 18 seconds. The sensitivity analyses show that time step is not a key in the accuracy of the meteorological values obtained. It is necessary consider the lack of data in METAR stations in Mexico.

In this work is defined a scheme of two stages for sending population segments of one Parallel Genetic Algorithm (PGA) to the nodes of an experimental grid called “Tarántula miniGrid”. The technique used to link the clusters and to configure the Tarántula mini Grid is described and the effects of latency in the communications between these clusters and the behavior of the speedup are discussed. The PGA is used for solving instances of the Vehicle Routing Problem with Time Windows (VRPTW). Results of this approach show that this communication scheme increases the efficiency of PGA in a grid environment and produces greater exploration and exploitation of the solution space of VRPTW.

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.